A top officer at the technology company IBM gave a presentation at the Elliott School of International Affairs Monday discussing the future of artificial intelligence in the workforce. The event, which featured Martin Fleming, the chief analytics officer and a chief economist at IBM, was the last installation of the Institute for International Economic Policy's 10th-anniversary speaker series, which ran throughout this academic year. Previous speakers included Louise Fox, a chief economist at the United States Agency for International Development and Bob Koopman, a chief economist at the World Trade Organization. Fleming began the event by describing artificial intelligence's increasingly important role in modern society – and its ability to structure typically unstructured data and make predictions about the future. He added that many forms of AI can improve productivity without killing human jobs – one of the most common fears about expanding robotics in the workforce.

The 45th US President was in your shoes before you and he is just doing fine. Or click on the pictures below to zoom in. Pedro URIA RECIO is thought-leader in artificial intelligence, data analytics and digital marketing. His career has encompassed building, leading and mentoring diverse high-performing teams, the development of marketing and analytics strategy, commercial leadership with P&L ownership, leadership of transformational programs and management consulting.

In previous articles, I've discussed how productized analytics can be a great fit for many companies seeking to accelerate the process of gaining business value from data. Too often, businesses approach analytics with the belief that they need a custom solution, that their particular challenges demand software as exceptional as the problem. But in truth, that just isn't the case for many issues, including analytics which can be productized efficiently and effectively. Productized analytics are a great gateway for companies that want to ramp up their analytics in a short amount of time. Of course, the product must fit your needs, and that's why I'm writing about this.

Raise your hand if your company is making more than 15! Well executed basic excel models might have brought quick wins in the 2000s but advanced analytics requires some time. Analytics is never plug & play because plugging data into models is extremely lengthy, because learnings are not transferable across companies or markets and because they require a high OPEX in people and high CAPEX in systems. Analytics is not about solutions looking for problems but problems looking for solutions. Questions such as "What can we do with blockchain?" do not make sense. "How can I solve my marketing problem" is a question that makes sense.